Noninvasive biomarkers to guide intervention: toward personalized patient management in prostate cancer

Predicting Clinically Significant Prostate Cancer Using Urine Metabolomics via Liquid Chromatography Mass Spectrometry

PURPOSE

Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles.

MATERIALS AND METHODS

Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion.

RESULTS

The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88-0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column.

CONCLUSIONS

Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Validation of diagnostic nomograms based on CE-MS urinary biomarkers to detect clinically significant prostate cancer

PURPOSE

Prostate cancer (PCa) is one of the most common cancers and one of the leading causes of death worldwide. Thus, one major issue in PCa research is to accurately distinguish between indolent and clinically significant (csPCa) to reduce overdiagnosis and overtreatment. In this study, we aim to validate the usefulness of diagnostic nomograms (DN) to detect csPCa, based on previously published urinary biomarkers.

METHODS

Capillary electrophoresis/mass spectrometry was employed to validate a previously published biomarker model based on 19 urinary peptides specific for csPCa. Added value of the 19-biomarker (BM) model was assessed in diagnostic nomograms including prostate-specific antigen (PSA), PSA density and the risk calculator from the European Randomized Study of Screening. For this purpose, urine samples from 147 PCa patients were collected prior to prostate biopsy and before performing digital rectal examination (DRE). The 19-BM score was estimated via a support vector machine-based software based on the pre-defined cutoff criterion of - 0.07. DNs were subsequently developed to assess added value of integrative diagnostics.

RESULTS

Independent validation of the 19-BM resulted in an 87% sensitivity and 65% specificity, with an AUC of 0.81, outperforming PSA (AUC _PSA: 0.64), PSA density (AUC _PSAD: 0.64) and ERSPC-3/4 risk calculator (0.67). Integration of 19-BM with the rest clinical variables into distinct DN, resulted in improved (AUC range: 0.82-0.88) but not significantly better performances over 19-BM alone.

CONCLUSION

19-BM alone or upon integration with clinical variables into DN, might be useful for detecting csPCa by decreasing the number of biopsies.

Prospective study of diagnostic accuracy in the detection of high-grade prostate cancer in biopsy-naïve patients with clinical suspicion of prostate cancer who underwent the Select MDx test

OBJECTIVES

This study aimed to externally validate the diagnostic accuracy of the Select MDx test for Significant prostate cancer (Sig PCa) (ISUP > 1), in a contemporaneous, prospective, multicenter cohort with a prostate-specific antigen (PSA) between 3 and 10 ng/ml and a non-suspicious digital rectal examination.

METHODS AND PARTICIPANTS

For all enrolled patients, the Select Mdx test, the risk calculator ERSPC3 + DRE, and a prostatic magnetic resonance imaging (MRI) were carried out. Subsequently, a systematic 12-core trans-rectal biopsy and a targeted biopsy, in the case of a prostate imaging-reporting and data system (PIRADS) > 2 lesion (max three lesions), were performed. To assess the accuracy of the Select MDx test in the detection of clinically Sig PCa, the test sensitivity was evaluated. Secondary objectives were specificity, negative predictive value (NPV), positive predictive value (PPV), and area under the curve (AUC). A direct comparison with the ERSPC + DRE risk calculator and MRI were also performed. We also studied the predictive ability to diagnose Sig PCa from the combination of the Select MDx test with MRI using clinical decision-curve analysis.

RESULTS

There were 163 patients enrolled after meeting the inclusion criteria and study protocol. The Select MDx test showed a sensitivity of 76.9% (95% CI, 63.2-87.5), 49.6% specificity (95% CI, 39.9-59.2), 82.09% (95% CI, 70.8-90.4) NPV, and 41.67% (95% CI, 31.7-52.2) PPV for the diagnosis of Sig PCa. COR analysis was also performed, which showed an AUC of 0.63 (95% CI, 0.56-0.71). There were no differences in the accuracy of Select MDx, ERSPC + DRE, or MRI. The combination of Select MDX + MRI showed the highest impact in the decision-curve analysis, with an NPV of 93%.

CONCLUSION

Our study showed a worse performance for the SelectMdx test than previously reported, within a cohort of patients with a PSA 3-10 ng/ml and a normal DRE, with results similar to those from ERSPC + DRE RC and MRI, but with an improvement in the usual PSA pathway. A combination of the Select Mdx test and MRI could improve accuracy, but studies specifically evaluating this scenario with a cost-effective analysis are needed.

Does Adding Standard Systematic Biopsy to Targeted Prostate Biopsy in PI-RADS 3 to 5 Lesions Enhance the Detection of Clinically Significant Prostate Cancer? Should All Patients with PI-RADS 3 Undergo Targeted Biopsy?

INTRODUCTION

Our aim was to assess the value of adding standard biopsy to targeted biopsy in cases of suspicious multiparametric magnetic resonance imaging (mp-MRI) and also to evaluate when a biopsy of a PI-RADS 3 lesion could be avoided.

METHODS

A retrospective study of patients who underwent targeted biopsy plus standard systematic biopsy between 2016-2019 was performed. All the 1.5 T magnetic resonance images were evaluated according to PI-RADSv.2. An analysis focusing on the clinical scenario, lesion location, and PI-RADS score was performed.

RESULTS

A total of 483 biopsies were evaluated. The mean age was 65 years, with a PSA density of 0.12 ng/mL/cc. One-hundred and two mp-MRIs were categorized as PI-RADS-3. Standard biopsy was most helpful in detecting clinically significant prostate cancer (csPCa) in patients in the active surveillance (AS) cohort (increasing the detection rate 12.2%), and in peripheral lesions (6.5%). Adding standard biopsy showed no increase in the detection rate for csPCa in patients with PI-RADS-5 lesions. Considering targeted biopsy in patients with PI-RADS 3 lesions, a higher detection rate was shown in biopsy-naïve patients versus AS and in patients with a previous negative biopsy (p = 0.002). Furthermore, in these patients, the highest rate of csPCa detection was in anterior lesions [42.9% (p = 0.067)].

CONCLUSIONS

Our results suggest that standard biopsy could be safely omitted in patients with anterior lesions and in those with PI-RADS-5 lesions. Targeted biopsy for PI-RADS-3 lesions would be less effective in peripheral lesions with a previous negative biopsy.